Multi-scale local shape analysis and feature selection in machine learning applications

Conference Paper

We introduce a method called multi-scale local shape analysis for extracting features that describe the local structure of points within a dataset. The method uses both geometric and topological features at multiple levels of granularity to capture diverse types of local information for subsequent machine learning algorithms operating on the dataset. Using synthetic and real dataset examples, we demonstrate significant performance improvement of classification algorithms constructed for these datasets with correspondingly augmented features.

Full Text

Duke Authors

Cited Authors

  • Bendich, P; Gasparovic, E; Harer, J; Izmailov, R; Ness, L

Published Date

  • September 28, 2015

Published In

  • Proceedings of the International Joint Conference on Neural Networks

Volume / Issue

  • 2015-September /

International Standard Book Number 13 (ISBN-13)

  • 9781479919604

Digital Object Identifier (DOI)

  • 10.1109/IJCNN.2015.7280428

Citation Source

  • Scopus